Estimating battery state of health with 10-min relaxation voltage across various charging states of charge

Abstract

Battery capacity assessment is a crucial research direction in the field of lithium-ion battery applications. In the previous research, a novel data-driven state of health (SOH) estimation method based on the voltage relaxation curve at full charging is developed. The experimental results have shown the evidence of the superiority of accurate battery SOH estimation based on physical features derived from equivalent circuit models (ECMs). However, the earlier research has limitations in estimating battery capacity with a diversity of battery charging states of charge. This study represents an extension of the previous work, aiming to investigate the feasibility of this technology for battery degradation evaluation under various charging states so that the application capability in practice is enhanced. In this study, six ECM features are extracted from 10-min voltage relaxation data across varying charging states to characterize the battery degradation evolution. Gaussian process regression (GPR) is employed to learn the relationship between the physical features and battery SOH. Experimental results under 10 different state of charge (SOC) ranges show that the developed methodology predicts accurate battery SOH, with a root mean square error being 0.9%.

Keywords

• battery state of health
• 10-min relaxation voltage
• varying charging states
• physical features
• machine learning